Real-time visualization is an actively growing area in different scientific areas. The medical field is not an exception, and tumors, vessels and organs are visualized more accurately as technology improves, and recently the potential to perform a real-time visualization has not only been possible but the addition of this technology have shown improved results in interventional procedures. Buddingh K T, et al. “Intraoperative assessment of biliary anatomy for prevention of bile duct injury: a review of current and future patient safety interventions.” Surg Endosc. 2011; 25:2449-61; Keereweer S, et al. “Optical image-guided surgery—where do we stand?” Mol Imaging Biol. 2011; 13:199-207; and Cannon J W, Stoll J A, et al. “Real-time three-dimensional ultrasound for guiding surgical tasks.” Comput Aided Surg. 2003; 8:82-90. Furthermore, during a real-time visualization and evaluation, prior analysis of a particular area or volume of interest could be imported, to assist in the current evaluation of the image. Nakano S, et al. “Fusion of MRI and sonography image for breast cancer evaluation using real-time virtual sonography with magnetic navigation: first experience.” Jpn J Clin Oncol. 2009; 39:552-9. Conventional techniques involve co-registration and segmentations algorithms.
Co-registration techniques display prior images, with their associated analysis, and import them as the real-time image, approximating its position and orientation based on software calculation. This position is approximated using different methods such as marking the patient (tattooing), placing the patient on the table in a very similar position as in the prior exam, or using real-time imaging (e.g., ultrasound co-registration) to approximate the area where the data should be imported. Regardless of the co-registration technique, this image is not the “real-time” image and any changes is position, manipulation of surrounding tissues or simple changes in tissue volumes (secondary to the pliability of the tissues) render this static, prior image inaccurate. Segmentation techniques are similarly powerful and allow the user to visualize a particular organ or area of interest in a user friendly fashion. These techniques recognize particular tissues based on their image intensities and can show them in a three-dimensional manner and some of them in an automatic fashion. Gao Y, et al. “Prostate segmentation by sparse representation based classification.” Med Image Comput Comput Assist Interv. 2012; 15:451-8; Liu X, et al. “Fully automatic 3D segmentation of iceball for image-guided cryoablation.” Conf Proc IEEE Eng Med Biol Soc. 2012; 2012:2327-30. The drawback of these techniques is the limited ability to import prior analysis, preventing useful prior evaluations to be considered during this real-time assessment.